Comparison Overview
State Land Bank Authority
State Land Bank Authority
undefined, Lansing, MI, undefined, US
Last Update: 17/12/2025
The State Land Bank works to create a positive economic impact on Michigan communities by recycling land to productive use. We work in a coordinated manner to foster the development of property to promote and support land bank operations at the county.
National Institute of Statistics and Geography of Mexico (INEGI)
Av. Héroe de Nacozari Sur 2301, Aguascalientes, 20276, MX
Last Update: 02/04/2026
The National Institute of Statistics and Geography of Mexico (INEGI) is an autonomous institution of the Federal Public Sector. INEGI is the coordinator of the National System of Statistical and Geographical Information of Mexico. The main objective of INEGI is to pr...
Compliance Ranges Comparison
State Land Bank Authority
National Institute of Statistics and Geography of Mexico (INEGI)
Benchmark & Cyber Underwriting Signals
Incidents vs Government Administration Industry Avg (This Year)
No incidents recorded for State Land Bank Authority in 2026.
Incidents vs Government Administration Industry Avg (This Year)
No incidents recorded for National Institute of Statistics and Geography of Mexico (INEGI) in 2026.
Incident History - State Land Bank Authority (X = Date, Y = Severity)
State Land Bank Authority cyber incidents detection timeline including parent company and subsidiaries.
Incident History - National Institute of Statistics and Geography of Mexico (INEGI) (X = Date, Y = Severity)
National Institute of Statistics and Geography of Mexico (INEGI) cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
State Land Bank Authority
National Institute of Statistics and Geography of Mexico (INEGI)
FAQ
Latest Global CVEs
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailable to all users.
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated attacker with low-privilege access can trigger a denial of service condition in Kibana by sending a specially crafted, oversized request payload. Processing this user-supplied input requires resource-intensive memory allocation that can exhaust the available heap memory in the Kibana process, causing it to crash and become unavailable to all users.
Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to information disclosure via user-supplied identifiers that reference scheduled query result data from Kibana Spaces the requester is not authorized to access.
Incorrect Authorization (CWE-863) in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management endpoint performs an insufficient authorization check. The endpoint validates only a coarse privilege level but does not verify that the requesting user has access to the specific Machine Learning job or notification resources provided in the request. As a result, a low-privileged user with Machine Learning access in any Kibana space can manipulate Machine Learning audit and notification records for arbitrary jobs—including jobs in other spaces or belonging to other users—by leveraging Kibana's internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.
Uncontrolled Recursion (CWE-674) in Elasticsearch can lead to denial of service via a specially crafted search request submitted by a low-privileged authenticated user. A user with read-level index access can submit a request that triggers unbounded recursive processing within the Elasticsearch query evaluation component, causing a fatal error that terminates the affected node. In single-node deployments, this results in complete service outage; in multi-node clusters, it causes repeated node restarts and sustained availability degradation.